Mountainous rural ecological geological environment safety quantitative analysis method and system

Abstract

The present application relates to the technical field of ecological geological environment safety assessment, and discloses a mountainous rural ecological geological environment safety quantitative analysis method and system.The method extracts four types of factors, i.e., geological stability, surface morphology, ecological vulnerability and social pressure, by interpreting regional observation data; constructs a multi-layer coupling analysis framework with ecological vulnerability factor as medium, in series with geological basement support, surface cover layer shaping and social disturbance source stress; further calculates the interaction index among the factors, generates a simulation scenario set of environment state evolution through dynamic deduction; and obtains a quantitative safety characterization result through reverse identification of the element combination mode of ecological vulnerability tending to stability.The method overcomes the limitations of traditional assessment methods, such as fuzzy element relationship and static early warning, through structured coupling analysis and dynamic scenario simulation.

Description

Technical Field

[0001] This invention relates to the field of ecological geological environment safety assessment technology, specifically a quantitative analysis method and system for ecological geological environment safety in mountainous rural areas. Background Technology

[0002] Ecological and geological environmental safety assessments in mountainous rural areas typically employ methods such as independent evaluation of single elements like geology, geomorphology, and ecology, or linear overlay and static weighting analysis of multiple evaluation indicators. Existing techniques treat geological stability, surface morphology, ecological vulnerability, and social activity pressures as parallel, independent variables, focusing on calculating current risk levels or conducting static zoning. These methods are insufficient in characterizing the systematic and hierarchical interactions between the geological foundation, surface overlying layers, ecological core, and human activities.

[0003] The shortcomings of existing technical solutions are mainly reflected in two aspects. The analytical methods fail to effectively construct a three-dimensional logical relationship between natural and social elements, making it difficult to clearly quantify the basic support of geological conditions for ecological processes, the structural shaping of ecological space by landforms, and the stress paths generated by human activities as external disturbance sources. The assessment process is mostly based on one-dimensional calculations of fixed models, which cannot simulate how the state of ecological vulnerability evolves dynamically under different combinations of elements and driving conditions. Furthermore, it lacks methods to reverse-engineer the key conditions and thresholds that make the system tend to stabilize from a massive number of possibilities, resulting in limited value for dynamic early warning and forward guidance of the assessment results.

[0004] There is a need for an analytical method that can address the shortcomings in the analysis of multi-level element coupling mechanisms and the lack of dynamic evolution scenario simulation capabilities. The purpose of this invention is to quantify the complex "support-shaping-stress" relationships among various elements by establishing a structured coupling analysis framework and dynamic deduction mechanism, and to generate environmental evolution paths and element combination patterns that can characterize the safety state. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for quantitative analysis of the ecological and geological environment safety in mountainous rural areas, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, this invention provides a method for quantitative analysis of ecological and geological environmental safety in mountainous rural areas, the method comprising:

[0007] Obtain raw observation data of the area to be analyzed, including geological bodies, landforms, vegetation, water systems, and human settlements.

[0008] The original observation data were interpreted to extract four basic quantitative factors: geological stability factors, landform factors, ecological vulnerability factors, and social pressure factors.

[0009] A multi-layered coupled analysis framework is constructed, with geological stability factors as the base, landform factors as the overlay, ecological vulnerability factors as the medium, and social pressure factors as the disturbance source.

[0010] Within the multi-layered coupling analysis framework, ecological vulnerability factors are used as the core transmission link to calculate the supporting strength of geological stability factors, the shaping effect of landform factors, and the stress index of social pressure factors.

[0011] Based on support strength, shaping effect and stress index, dynamic simulation is carried out within a multi-layered coupled analysis framework to generate a set of simulated scenarios that reflect the evolution path of environmental state under different combinations of factors.

[0012] The combination patterns of elements that stabilize ecological vulnerability factors are identified from the set of simulated scenarios. The specific numerical ranges of various basic quantitative factors corresponding to the combination patterns are determined as the quantitative characterization results of ecological geological environment safety.

[0013] Preferably, the element interpretation of the original observation data specifically includes:

[0014] Based on the original observation data related to geological bodies, the types of rock and soil bodies, structural features and groundwater occurrence characteristics are identified. The identification results are quantified into rock and soil strength values, tectonic activity indicators and aquifer water-bearing levels, which are then combined to form the geological stability factor.

[0015] Based on the original observation data related to landforms, the topographic relief, gully cutting density and slope structure characteristics are analyzed. The analysis results are quantified into topographic complexity, gully density and slope stability scores, which are then combined to form the landform factors mentioned above.

[0016] Based on the original observation data related to vegetation and water system, vegetation community coverage, biodiversity index, river network stability and water body self-purification capacity are assessed. The assessment results are quantified as vegetation coverage, species abundance, river network stability coefficient and water quality maintenance coefficient, which are combined to form the ecological vulnerability factors mentioned above.

[0017] Based on the original observation data related to human settlement elements, the population density, land use intensity, scale of engineering activities and pollution emission load are statistically analyzed. The statistical results are quantified into population pressure value, land use index, engineering disturbance intensity and pollution load index, which are combined to form the social pressure factor.

[0018] Preferably, calculating the support strength of the geological stability factor for the ecological vulnerability factor includes:

[0019] Establish a correlation mapping between the soil and rock strength values, tectonic activity indicators and the vegetation coverage and species abundance;

[0020] Through correlation mapping analysis, the lower limit of soil and rock strength and the upper limit of tectonic activity index required to maintain the preset levels of vegetation cover and species abundance were determined.

[0021] The support strength value of the geological stability factor for the ecological vulnerability factor is calculated by taking into account the extent to which the actual soil and rock strength value exceeds the lower limit and the extent to which the actual tectonic activity index is lower than the upper limit.

[0022] Preferably, calculating the shaping effect of landform factors on the ecological vulnerability factors includes:

[0023] Establish a correlation mapping between the terrain complexity, gully density, and slope stability scores and the river network stability coefficient and water quality maintenance coefficient;

[0024] Through correlation mapping analysis, the influence trends of different combinations of terrain complexity, gully density and slope stability scores on river network stability coefficient and water quality maintenance coefficient were evaluated.

[0025] Based on this trend, the comprehensive shaping effect of the current landform factor configuration on the river network stability coefficient and water quality maintenance coefficient among the ecologically vulnerable factors is calculated.

[0026] Preferably, calculating the stress index of social stress factors on the ecological vulnerability factors includes:

[0027] Establish a correlation mapping between the population pressure value, land use index, engineering disturbance intensity and pollution load index and the vegetation coverage, species abundance, river network stability coefficient and water quality maintenance coefficient;

[0028] Through correlation mapping analysis, the rate of decline of vegetation cover, species abundance, river network stability coefficient or water quality maintenance coefficient caused by the increase of pressure value per unit population, land use index, engineering disturbance intensity or pollution load index is quantified.

[0029] Based on the specific values ​​of current social stress factors and the decay rate, a weighted comprehensive stress index of social stress factors on ecological vulnerability factors is calculated.

[0030] Preferably, the dynamic simulation based on support strength, shaping effect, and stress index within a multi-layered coupled analysis framework generates a set of simulated scenarios reflecting the evolution path of environmental state under different combinations of factors, specifically including:

[0031] Within the aforementioned multi-layered coupling analysis framework, the values ​​of the geological stability factor, landform factor, and social pressure factor are set to vary within their possible ranges according to a set step size.

[0032] For each combination of changing factor values, the corresponding support strength, shaping effect, and stress index are recalculated according to the calculation methods for the support strength, the shaping effect, and the stress index.

[0033] The recalculated support strength, shaping effect, and stress index are used as inputs to drive the iterative calculation of each quantitative indicator in the ecological vulnerability factor according to the preset dynamic rules.

[0034] Record the state change trajectory of the ecological vulnerability factor during each iteration calculation, and construct the simulation scenario set by combining all factor values ​​and their corresponding state change trajectories.

[0035] Preferably, the step of identifying the combination patterns of elements that tend to stabilize ecologically vulnerable factors from the set of simulated scenarios includes:

[0036] In the set of simulated scenarios, all simulated scenarios in which the fluctuation range of each quantitative indicator of the ecological vulnerability factor is less than the preset threshold after the iterative calculation is completed are selected.

[0037] The numerical distribution patterns of geological stability factors, landform factors, and social pressure factors in the selected simulation scenarios were analyzed.

[0038] The numerical ranges of various factors that exhibit a high-frequency concentration trend in the numerical distribution pattern are defined as the element combination pattern.

[0039] Preferably, after constructing the simulated scenario set, a verification step is also included:

[0040] Actual observational data of the region to be analyzed during historical periods are selected and interpreted as basic quantitative factors for historical periods.

[0041] The basic quantitative factor values ​​of historical periods are input into the multi-layered coupled analysis framework for dynamic simulation to obtain historical simulation scenarios.

[0042] The evolution trajectory of ecologically vulnerable factors in historical simulation scenarios is compared with the actual ecological conditions recorded in historical periods, and the degree of trajectory consistency is calculated.

[0043] If the trajectory matching degree reaches the preset standard, the multi-layer coupling analysis framework and dynamic inference are deemed effective; otherwise, the dynamic rules are adjusted and the dynamic inference is repeated until the trajectory matching degree reaches the standard.

[0044] Preferably, the acquisition of raw observation data of the area to be analyzed, including geological bodies, landforms, vegetation, water systems, and human settlement elements, specifically includes:

[0045] Acquire regional multispectral and radar imagery data through satellite remote sensing platforms;

[0046] Geological drilling, soil sampling, vegetation quadrat and water quality monitoring data were obtained through a ground survey network;

[0047] Data on population and economy, land use planning, and infrastructure construction projects are obtained through social information statistics systems.

[0048] Data from different sources are registered and fused in a unified spatiotemporal coordinate system to form spatiotemporally consistent original observation data.

[0049] Preferably, the present invention also includes a quantitative analysis system for the ecological and geological environment safety of mountainous villages. The system includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the quantitative analysis method for the ecological and geological environment safety of mountainous villages as described above.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] By constructing a multi-layered coupled analysis framework with geological stability factors as the base, landform factors as the overlay, ecological vulnerability factors as the medium, and social pressure factors as the disturbance source, the hierarchy and functional role of each factor in the system are clarified. This framework establishes a hierarchical interaction relationship between geological stability, landform, ecological vulnerability, and social pressure, transforming the traditionally parallel and mixed indicator system into a structured network with causal transmission paths. This enables the analysis of the ecological geological environment system to delve deeper from the description of surface phenomena to the level of intrinsic mechanisms, accurately expressing how the geological foundation supports ecological processes, how landform configures ecological space, and how social activities exert pressure through direct or indirect means.

[0052] Within the constructed analytical framework, this method comprehensively calculates the supporting strength of geological stability, the shaping effect of landform, and the stress index of social pressure, using ecological vulnerability factors as a link, and then dynamically extrapolates based on these conditions. This method can generate a set of possible evolutionary paths for the ecosystem state under different initial conditions and disturbance scenarios. From this scenario set, it is possible to inversely identify which combinations of factor values ​​can lead to a stable state of ecological vulnerability. This changes the traditional judgment model that relies on a single safety threshold or static conclusion, transforming safety assessment from labeling the current state to extrapolating multiple possibilities and their evolutionary conditions, thus enhancing the pertinence and predictability of the assessment results. Attached Figure Description

[0053] Figure 1 This is a schematic diagram illustrating the working principle of the quantitative analysis method for ecological and geological environmental safety in mountainous rural areas as described in this invention.

[0054] Figure 2 A flowchart for interpreting elements;

[0055] Figure 3 A flowchart for calculating the support strength of geological stability factors;

[0056] Figure 4 A graph showing the quantitative results of the coupled analysis of the ecological and geological environment in mountainous rural areas;

[0057] Figure 5 Box plot of scenario stability of ecological and geological environmental factors in mountainous rural areas. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Please see Figure 1 This invention provides a method for quantitative analysis of ecological and geological environmental safety in mountainous rural areas. The method includes: acquiring original observation data of the area to be analyzed, including geological bodies, landforms, vegetation, water systems, and human settlements; interpreting the original observation data to extract four basic quantitative factors: geological stability factors, landform factors, ecological vulnerability factors, and social pressure factors; constructing a multi-layered coupled analysis framework with geological stability factors as the analysis basis, landform factors as the overlying layer, ecological vulnerability factors as the medium, and social pressure factors as the disturbance source; within this framework, ecological vulnerability factors are regarded as the core transmission link, and the support strength of geological stability factors, the shaping effect of landform factors, and the stress index of social pressure factors are calculated; based on the calculated support strength, shaping effect, and stress index, dynamic deduction is performed within the multi-layered coupled analysis framework to generate a set of simulated scenarios that can reflect the evolution path of environmental state under different combinations of factors; identifying the factor combination patterns that can stabilize ecological vulnerability factors from the set of simulated scenarios, and determining the specific numerical ranges of various basic quantitative factors corresponding to these patterns as the quantitative characterization results of ecological and geological environmental safety.

[0060] In one embodiment of the present invention, see [reference] Figure 2Multispectral imagery acquired through satellite remote sensing platforms is used to identify vegetation types and cover, while radar imagery data is used to invert surface deformation and topographic features. For example, multispectral imagery data is processed to calculate the Normalized Difference Vegetation Index (NDVI), which serves as the basic input for assessing vegetation community cover; radar imagery data is processed through interferometry to generate digital elevation models (DEMs), which are direct data sources for analyzing topographic relief and valley morphology. Core samples from geological drilling are obtained through a ground survey network, and the samples undergo indoor mechanical tests to determine uniaxial compressive strength, which serves as a direct quantitative basis for soil and rock strength. Soil sampling points are set up under different land use types to analyze soil texture and heavy metal content; vegetation quadrat surveys record species quantity and individual density for calculating biodiversity indices; water quality monitoring points are set up along water systems to measure indicators such as chemical oxygen demand (COD) and ammonia nitrogen to assess the self-purification potential of water bodies. The population density of townships is obtained from the population census data through the social information statistics system, which is used to quantify the population pressure value; the obtained land use status map is used to calculate the proportion of construction land and generate the land use index; the collected list of engineering projects is classified into levels according to their land area and earthwork volume, and converted into engineering disturbance intensity; the pollution source census data recorded by the environmental protection department is used to calculate the pollutant emissions per unit area and generate the pollution load index.

[0061] In some embodiments, the interpretation of original observation data for geological bodies is based on regional geological maps and field survey records. The identification of rock and soil types categorizes exposed strata and lithology into types such as granite, limestone, and loose deposits. Granite rock and soil strength values ​​are assigned to the range of 80-120 MPa based on their weathering degree, while limestone rock and soil strength values ​​are assigned to the range of 60-100 MPa based on the degree of karst development. The identification of tectonic features focuses on the distribution density of active faults and historical earthquake magnitudes. Tectonic activity indicators are quantified using a weighted average of fault density and peak ground acceleration. The identification of groundwater occurrence characteristics is based on hydrogeological borehole data, and aquifer water-bearing levels are classified into three levels: abundant, moderate, and scarce, based on unit yield. The quantified rock and soil strength values, tectonic activity indicators, and aquifer water-bearing levels are combined to constitute a geological stability factor.

[0062] In some embodiments, the analysis of raw geomorphological observation data is based on digital elevation models and remote sensing imagery. Topographic relief is analyzed by calculating the difference between the highest and lowest elevations within a specific window. Topographic complexity is quantified using a combination of topographic relief and surface incision depth. Gully incision density is analyzed by obtaining the river network through a drainage extraction algorithm, and gully density is quantified by the length of gullies per unit area. Slope structure characteristics are analyzed by identifying slope type and the dip of weak structural surfaces. Slope stability scores are semi-quantitatively assigned based on the relationship between slope angle, aspect, and stratum dip. For example, slopes with a dip angle greater than 25 degrees have lower stability scores, while slopes with a dip angle less than 15 degrees have higher stability scores. The quantified topographic complexity, gully density, and slope stability scores are combined to constitute the landform factor.

[0063] Optionally, the interpretation and assessment of original vegetation and water system observation data integrates remote sensing inversion and ground-based measurements. Vegetation community cover is assessed using a pixel-based binary model to process normalized vegetation index data, with vegetation cover quantified as a percentage. Biodiversity index assessment is based on quadrat survey data, with species richness quantified using the Shannon-Wiener index. River network stability assessment considers riverbed composition and bank erosion rates; the river network stability coefficient is constructed using the inverse relationship between channel shift frequency and sedimentation rate. Water body self-purification capacity assessment is based on water quality monitoring data and hydrological parameters; the water quality maintenance coefficient is correlated with the chemical oxygen demand (COD) degradation rate and water renewal cycle. The quantified vegetation cover, species richness, river network stability coefficient, and water quality maintenance coefficient are combined to constitute the ecological vulnerability factor.

[0064] Optionally, the interpretation of raw observation data on human settlement elements is based on socio-economic data and planning information. Population density is calculated at the administrative village level, and population pressure is normalized using the ratio of population density to resource carrying capacity. Land use intensity is calculated based on the proportion of construction land and agricultural land using remote sensing classification maps, and the land use index is obtained by weighting the ecological impact coefficients of various land use types. The scale of engineering activities is compiled from recent highway, residential expansion, and mining projects, with the intensity of engineering disturbance assigned according to the area and depth of the project's impact. Pollution load is compiled from the discharge of domestic sewage, agricultural non-point sources, and scattered industrial points, and the pollution load index is quantified as the standard pollution load carried per unit watershed area.

[0065] It is understandable that the registration and fusion of data from different sources involves the unification of spatial benchmarks and attribute association. Satellite remote sensing imagery, digital elevation models, and land use maps are unified to the same geographic coordinate system and projected coordinate system. Ground survey point data are matched to the spatial base map using GPS coordinates. Socioeconomic statistics are spatially associated and registered based on administrative boundary vector surfaces. Under a unified spatiotemporal coordinate system, a grid cell may contain vegetation cover data retrieved from remote sensing, topographic complexity data derived from digital elevation models, soil and rock strength values ​​derived from geological maps, and population pressure values ​​interpolated from demographic statistics. All rasterized data layers undergo preliminary spatiotemporal consistency fusion verification using the following formula:

[0066]

[0067] Where: symbol Represents the consistency check coefficient, symbol Represents the total number of data layers participating in the fusion, symbol Representing the The value of each data layer at the reference cell, sign Representing the The numerical values ​​of each data layer at the pixel to be registered, with symbols... Representing the Numerical range of each data layer. Verification coefficients. Data layers below a certain threshold are considered spatiotemporally consistent and form the raw observation data ultimately used for interpretation.

[0068] It is understandable that acquiring and interpreting raw observation data constitutes a systematic data preprocessing process. Satellite remote sensing platforms provide large-scale, periodic land cover and morphological information, ground survey networks provide precise physicochemical and biological information at the point scale, and social information statistical systems provide information on the intensity and distribution of human activities. Interpreting this heterogeneous data into four categories of basic quantitative factors—geological stability factors, landform factors, ecological vulnerability factors, and social pressure factors—is a necessary step to provide standardized input for the subsequent construction of a multi-layered coupled analysis framework. The quantification process transforms qualitative geological descriptions, geomorphological features, ecological conditions, and socioeconomic data into comparable and calculable numerical or hierarchical indicators.

[0069] In one embodiment of the present invention, see [reference] Figure 3This study establishes a correlation mapping between soil and rock strength values ​​and tectonic activity indices (geological stability factors) and vegetation cover and species abundance (ecological vulnerability factors). This process is based on historical observation data and mechanistic understanding to construct a relational model. For example, in a regional unit dominated by granite, historical observations show that vegetation cover has remained above 70% and species abundance above 2.5, while soil and rock strength values ​​are generally greater than 95 MPa and tectonic activity indices are less than 0.3. In another regional unit dominated by loose sediments, historical observations show that vegetation cover fluctuates between 40% and 50% and species abundance is below 1.8, while soil and rock strength values ​​are less than 30 MPa and tectonic activity indices are greater than 0.7. By analyzing and pairing historical data from multiple such regional units, the correlation mapping relationship is established.

[0070] In some embodiments, correlation mapping analysis is used to determine the lower limit of soil and rock strength values ​​and the upper limit of tectonic activity indices required to maintain preset levels of vegetation cover and species abundance. The preset levels of vegetation cover and species abundance are set based on the regional ecological baseline; for example, the preset level of vegetation cover is set to 60%, and the preset level of species abundance is set to 2.0. The correlation mapping dataset is analyzed, and all data records with vegetation cover greater than or equal to 60% and species abundance greater than or equal to 2.0 are selected. The minimum value in the set of soil and rock strength values ​​corresponding to these records is determined as the lower limit of soil and rock strength values, and the maximum value in the set of tectonic activity indices corresponding to these records is determined as the upper limit of tectonic activity indices. If the analysis finds that the soil and rock strength values ​​of all data records meeting the above ecological preset conditions are greater than or equal to 80 MPa, then the lower limit of soil and rock strength values ​​is determined to be 80 MPa; if the tectonic activity indices of these records are all less than or equal to 0.5, then the upper limit of tectonic activity indices is determined to be 0.5.

[0071] In some embodiments, the supporting strength value of the geological stability factor to the ecological vulnerability factor is calculated by combining the degree to which the actual soil and rock strength value exceeds the lower limit and the degree to which the actual tectonic activity index is lower than the upper limit. For a specific assessment unit, its actual soil and rock strength value is a specific measured or estimated value, and its actual tectonic activity index is also a specific measured or estimated value. The degree to which the actual soil and rock strength value exceeds the lower limit is expressed as a difference ratio; similarly, the degree to which the actual tectonic activity index is lower than the upper limit is also expressed as a difference ratio. The supporting strength value of the geological stability factor is a comprehensive function of these two ratios. Calculated using the following formula:

[0072]

[0073] Where: symbol The numerical value representing the support strength of geological stability factors for ecological vulnerability factors, with the sign... The actual soil and rock strength value of the assessment unit, symbol This represents the lower limit of the soil and rock strength value determined through correlation mapping analysis, with the symbol... The symbol represents the actual construction activity index of the evaluation unit. The symbol represents the upper limit of the construction activity index determined through correlation mapping analysis. and These are weighting coefficients used to reflect the relative importance of soil and rock strength and tectonic activity in supporting ecologically vulnerable factors, and they satisfy the following conditions: When the actual soil and rock strength value Equal to or below the lower limit When the first term of the formula is zero or negative, the actual construction activity index is zero. Equal to or higher than the upper limit When the second term of the formula is zero or negative, the second term takes the value of zero.

[0074] Optionally, the correlation mapping can be established using a statistical regression model. A sufficient number of sample points are collected, each containing four values: soil and rock strength, tectonic activity index, vegetation cover, and species abundance. Using vegetation cover and species abundance as state variables, and soil and rock strength and tectonic activity index as explanatory variables, a multiple regression model is fitted. From the fitted model, the critical ranges of soil and rock strength and tectonic activity index under given target values ​​of vegetation cover and species abundance can be calculated. The lower and upper bounds of these critical ranges correspond to the required lower limit for soil and rock strength and the upper limit for tectonic activity index, respectively.

[0075] Optionally, the weights can be determined based on expert scoring and historical scenario inversion. Experts in geology and ecology independently score the relative importance of soil and rock strength and tectonic activity in maintaining regional vegetation and biodiversity stability. The scoring results are then normalized to obtain initial weights. Furthermore, multiple historical change cases are selected, and the weight coefficients are adjusted to ensure that the calculated support strength values ​​have the highest correlation with historically observed trends in ecological stability evolution. This process is used to calibrate and determine the final weight coefficients. and The value.

[0076] It is understandable that calculating the supporting strength of geological stability factors to ecological vulnerability factors is a key step in quantifying the geological foundation's ability to support the ecosystem. Correlation mapping analysis makes the implicit relationship between geological conditions and ecological state explicit and quantifiable. Determining the lower limit of soil and rock strength values ​​and the upper limit of tectonic activity indicators provides a clear benchmark for the assessment. The formula for calculating the supporting strength value compares and synthesizes the actual geological conditions of the assessment unit with the benchmark conditions, generating a comprehensive scalar value characterizing the relative strength of supporting capacity. This supporting strength value will serve as a core input parameter for subsequently measuring the role of the geological basement within a multi-factor coupled analysis framework.

[0077] It is understandable that the calculation of support strength relies entirely on the basic quantitative factor values ​​obtained from the previous element interpretation. The actual soil and rock strength values ​​originate from the geological body interpretation results, as do the actual tectonic activity indicators. The preset levels of vegetation cover and species abundance are linked to regional ecological management goals. The entire calculation process does not introduce new observational data; instead, based on existing quantitative factor data, it derives new coupling relationship indicators through established mapping rules and calculation formulas. This derived support strength indicator physically represents the potential or degree of guarantee for maintaining a preset level of ecological stability in the region under current geological conditions.

[0078] In one embodiment of the present invention, when calculating the shaping effect of landform factors on ecological vulnerability factors, a correlation mapping is established between topographic complexity, gully density, and slope stability scores in the landform factors and river network stability coefficient and water quality maintenance coefficient in the ecological vulnerability factors. For example, analysis of historical data reveals that in regional units with high topographic complexity, high gully density, and low slope stability scores, the river network stability coefficient is usually low, river channel shifting is recorded more frequently, and the water quality maintenance coefficient is also low, with historical monitoring values ​​of water turbidity being high. Conversely, in regional units with gentle topography, sparse gullies, and high slope stability scores, the river network stability coefficient and water system number are usually maintained at a high level. By organizing the quantitative values ​​of landform factors and the corresponding historical observation values ​​of ecological vulnerability factors from multiple regional units, a dataset for correlation mapping analysis can be formed.

[0079] In some embodiments, correlation mapping analysis is used to assess the impact trends of different combinations of topographic complexity, gully density, and slope stability scores on the river network stability coefficient and water quality maintenance coefficient. Multivariate statistical analysis is employed to process the dataset, identifying the statistical relationships between various landform factor indicators and the target indicators of ecological vulnerability factors. For example, a regression model is fitted with topographic complexity, gully density, and slope stability scores as independent variables and the river network stability coefficient as the dependent variable. The regression coefficients of the model represent the direction and intensity of the impact of each topographic factor on river network stability. Similarly, another regression model is established with the water quality maintenance coefficient as the dependent variable. The impact trends are specifically reflected in the positive or negative correlations indicated by these regression coefficients and their numerical magnitudes.

[0080] In some embodiments, the comprehensive shaping effect evaluation value on the river network stability coefficient and water quality maintenance coefficient of the ecological vulnerability factor under the current landform factor configuration is calculated based on the assessed impact trend. For a unit to be evaluated, its current topographic complexity, gully density, and slope stability score are known input values. The comprehensive shaping effect evaluation value is calculated using an aggregation function that integrates the values ​​of each landform index and their impact trend weights on the target index of the ecological vulnerability factor. Comprehensive shaping effect evaluation value of landform factors Calculated using the following formula:

[0081]

[0082] Where: symbol The symbol represents the comprehensive shaping effect evaluation value of landform factors. Represents terrain complexity, symbol Represents gully density, symbol Represents the slope stability score, function The symbol represents a function that undergoes standardized transformation based on the influence trend. , , These represent the influence coefficients obtained through correlation mapping analysis of terrain complexity, gully density, and slope stability scores, respectively. (Symbols: ...) , , It is a weighting coefficient that reflects the relative importance of local surface morphology indicators in their shaping role.

[0083] Optionally, when calculating the stress index of social stress factors on ecological vulnerability factors, a correlation mapping is established between the population stress value, land use index, engineering disturbance intensity, and pollution load index in the social stress factors and the vegetation cover, species abundance, river network stability coefficient, and water quality maintenance coefficient in the ecological vulnerability factors. The establishment of this mapping relationship relies on the analysis of historical contemporaneous or continuous monitoring data, analyzing the values ​​or rates of change of each indicator of the ecological vulnerability factors at specific social stress factor levels. For example, comparing two adjacent years with continuously increasing population stress values, the changes in vegetation cover and species abundance during the same period are observed; comparing two similar areas with different engineering disturbance intensities, the differences in their river network stability coefficient and water system number are observed.

[0084] Optionally, correlation mapping analysis can be used to quantify the rate of decline in vegetation cover, species abundance, river network stability coefficient, or water quality maintenance coefficient caused by an increase in each unit of population stress, land use index, engineering disturbance intensity, or pollution load index. The decline rate is obtained through difference or differential analysis of the correlation mapping data. For example, if vegetation cover decreases from 65% to 60% as the population stress value increases from 10 to 20, the rate of decline in vegetation cover caused by an increase in each unit of population stress value within this interval can be calculated as -0.5% per unit. Performing this type of analysis on each combination of social stress indicator and each ecological vulnerability indicator yields a decline rate matrix.

[0085] It is understandable that, based on the specific values ​​of current social pressure factors and their decay rates, a weighted comprehensive stress index of social pressure factors on ecological vulnerability factors is calculated. The calculation of the comprehensive stress index requires summarizing the potential or actual impacts of various social pressure factor indicators on ecological vulnerability factors. For an assessment unit, its current population pressure value, land use index, engineering disturbance intensity, and pollution load index are used as inputs. Each social pressure indicator value is multiplied by its corresponding decay rate for each ecological vulnerability indicator, and then the impacts on the ecological vulnerability indicators are summed. Finally, the impacts of different social pressure indicators are weighted and aggregated to obtain the comprehensive stress index. The formula is expressed as a weighted linear combination of the products of each decay rate and the pressure value.

[0086] It is understandable that the shaping effect evaluation value of landform factors and the stress index of social pressure factors are key intermediate variables for quantifying the impact of different driving forces on ecologically vulnerable factors. The shaping effect evaluation value quantifies the long-term, relatively stable impact of natural conditions such as topography on the formation of water system structure and function. The stress index quantifies the immediate or cumulative stress intensity of human activities on various components of the ecosystem. These two calculation results, together with the aforementioned geological stability factor support strength values, constitute the core driving force input set for subsequent dynamic extrapolation within the multi-factor coupled analysis framework, representing the degree of influence of forces from the geological base, landform, and socio-economic aspects on ecologically vulnerable media.

[0087] In one embodiment of the present invention, within a multi-layer coupled analysis framework, the values ​​of geological stability factors, landform factors, and social pressure factors are set to vary within their possible ranges at predetermined step sizes. For example, the lower limit of the soil and rock strength value in the geological stability factors is set to 80 MPa, the upper limit to 120 MPa, and the step size is set to 5 MPa; the lower limit of the tectonic activity index is set to 0.1, the upper limit to 0.8, and the step size is set to 0.1; the lower limit of the topographic complexity value in the landform factors is set to 10, the upper limit to 100, and the step size is set to 10; the lower limit of the gully density value is set to 1 km / km², the upper limit to 10 km / km², and the step size is set to 1 km / km²; the lower limit of the population pressure value in the social pressure factors is set to 0.2, the upper limit to 1.5, and the step size is set to 0.2. All step size variations of the above factors are combined to form a set of factor value combinations.

[0088] In some embodiments, for each combination of varying factor values, the corresponding support strength, shaping effect, and stress index are recalculated based on the calculation methods for support strength, shaping effect, and stress index. Each recalculation is an independent process, with the specific values ​​of the geological stability factor, landform factor, and social pressure factor in the current factor value combination as input, and the corresponding support strength value, comprehensive shaping effect evaluation value, and comprehensive stress index as output.

[0089] In some embodiments, the recalculated support strength, shaping effect, and stress index are used as inputs to drive iterative calculations of each quantitative indicator in the ecological vulnerability factor according to preset dynamic rules. The preset dynamic rules define the changes in vegetation cover, species abundance, river network stability coefficient, and water quality maintenance coefficient in the ecological vulnerability factor at each simulation time step. These changes are functions of the current values ​​of each ecological vulnerability factor, the currently input geological stability factor support strength value, the comprehensive shaping effect evaluation value of the landform factor, and the comprehensive stress index of the social pressure factor. The ecological vulnerability factor changes over time... The state is defined by the following difference equation:

[0090]

[0091] Where: symbol and These represent ecologically vulnerable factors over time. and The state vector contains four components: vegetation cover, species abundance, river network stability coefficient, and water quality maintenance coefficient. The symbols are... Represents the preset simulation time step, symbol The numerical value of the support strength representing the geological stability factor, with the symbol... The symbol represents the comprehensive shaping effect evaluation value of landform factors. The comprehensive stress index, representing social stress factors, is a function. This represents the state transition function defined according to dynamic rules. Iterative calculations begin from the initial state... Start and continue until the preset simulation termination time or state convergence condition is reached. See Table 1.

[0092] Table 1: Examples of numerical combinations of factors in simulated scenario construction

[0093]

[0094] Optional, the state transition function in the dynamic rules Specifically, this can be described as the sum of the rates of change of various ecological vulnerability indicators. The rate of change of vegetation cover is set to be positively correlated with the value of support strength and negatively correlated with the comprehensive stress index; the rate of change of species abundance is set to be positively correlated with the value of support strength and negatively correlated with the comprehensive stress index; the rate of change of river network stability coefficient is set to be positively correlated with the value of comprehensive shaping effect and negatively correlated with the comprehensive stress index; the rate of change of water quality maintenance coefficient is set to be positively correlated with the value of comprehensive shaping effect and negatively correlated with the comprehensive stress index. The specific mathematical form of the rate of change can be a linear function or a nonlinear saturated function.

[0095] Optional, initial state for iterative calculus The initial values ​​can be set based on the current observational baseline values ​​of the area to be analyzed. For example, the recent average vegetation cover of the study area can be used as the initial value of vegetation cover, the recent average species abundance can be used as the initial value of species abundance, the recent average stability coefficient of hydrological observations can be used as the initial value of river network stability coefficient, and the recent average maintenance coefficient of water quality monitoring can be used as the initial value of water quality maintenance coefficient.

[0096] It is understandable that recording the state change trajectory of ecological vulnerability factors during each iterative calculation, and constructing a set of simulation scenarios from all factor value combinations and their corresponding state change trajectories, is crucial. For each factor value combination, its corresponding state change trajectory is a time series data point, recording the state from its initial state... Initially, after each simulation time step... Throughout the simulation, the simulation scenario set contains continuous values ​​for vegetation cover, species abundance, river network stability coefficient, and water quality maintenance coefficient. The simulation scenario set is a structured dataset whose core fields include a unique identifier for the combination of factor values, the specific values ​​of geological stability factors, landform factors, and social pressure factors within the combination, and the trajectory of state changes of the associated ecologically vulnerable factors.

[0097] It is understandable that the entire dynamic simulation process is a systematic computational simulation. By systematically changing the input values ​​of geological stability factors, landform factors, and social pressure factors in step sizes across the entire parameter space, it is possible to exhaustively or extensively cover various possible combinations of natural and human conditions. For each input combination, by calculating its coupling forces and driving the dynamic changes of ecological vulnerability factors, the potential evolutionary paths of the ecological environment under different driving force configurations can be revealed. The simulation scenario set ultimately brings together all these "hypothesis-simulation" results.

[0098] See Figure 4 This is a quantitative result diagram of the coupled analysis of the ecological and geological environment in mountainous rural areas. It visually presents the combined influence of three types of factors—geology, surface, and society—on the ecological environment, representing the core quantitative outcome of the "multi-layered coupled analysis framework." The supporting / shaping effect of scenario A-5 is significantly higher than the stress index, indicating that its corresponding factor combination is more conducive to ecological stability. Through comparison of different scenarios, the positive effects of "enhancing geological stability and optimizing surface morphology" on alleviating social pressure stress can be clearly identified, providing direction for the safe regulation of the ecological and geological environment. By observing the synchronous changes of the three types of effects under different scenarios, the coupling law of "increased geological intensity → enhanced supporting effect → partial offsetting of social stress" is revealed, providing a basis for the analysis of the dynamic mechanism of ecological and environmental evolution.

[0099] In one embodiment of the present invention, the process of identifying the combination patterns of elements that stabilize ecological vulnerability factors from a set of simulated scenarios first requires screening all simulated scenarios in the set of simulated scenarios where the fluctuation range of each quantitative indicator of ecological vulnerability factors is less than a preset threshold after iterative calculation. The preset threshold is set according to management objectives, for example, the standard deviation of vegetation cover, species abundance, river network stability coefficient, and water quality maintenance coefficient in the last ten simulation time steps must be less than 5% of their respective initial values. The set of simulated scenarios is then traversed, and the fluctuation range of each ecological vulnerability indicator under each scenario at the end of the simulation is calculated. Scenario records where the fluctuation range of all indicators is lower than the threshold are retained.

[0100] In some embodiments, the numerical distribution patterns of geological stability factors, landform factors, and social pressure factors in the selected simulation scenarios are analyzed. For all retained scenario records, the specific values ​​of the input geological stability factors, landform factors, and social pressure factors are extracted. Statistical analysis is performed on the values ​​of each factor, such as plotting frequency distribution histograms or calculating the central tendency and dispersion of the values. It is observed whether the values ​​of geological stability factors are concentrated in the range of higher soil and rock strength values ​​and lower tectonic activity indices; whether the values ​​of landform factors are concentrated in the range of moderate terrain complexity, lower gully density, and higher slope stability scores; and whether the values ​​of social pressure factors are concentrated in the range of lower population pressure values, lower land use index, lower engineering disturbance intensity, and lower pollution load index.

[0101] In some embodiments, the numerical intervals of various factors exhibiting a high-frequency concentration trend in the numerical distribution pattern are defined as element combination patterns. For example, if in the selected stable scenarios, more than 80% of the scenarios have soil and rock strength values ​​falling within the [90, 110] MPa interval, and more than 80% of the scenarios have tectonic activity indices falling within the [0.1, 0.3] interval, then the pattern interval of the geological stability factor is defined as soil and rock strength values ​​of [90, 110] MPa and tectonic activity indices of [0.1, 0.3]. Similarly, high-frequency concentration intervals for each indicator in the landform factor and social pressure factor are defined respectively. These intervals together constitute an element combination pattern that stabilizes the ecological vulnerability factor, i.e., a multidimensional parameter space range.

[0102] Optionally, the verification step is performed after constructing the simulation scenario set. Actual observational data of the area to be analyzed in historical time periods are selected and interpreted as basic quantitative factors for historical periods. Remote sensing images, geological survey reports, hydrological monitoring data, and population and economic statistics of the area to be analyzed, recorded annually over the past ten years, are collected. Following the method described in the embodiments, the specific values ​​of geological stability factors, landform factors, ecological vulnerability factors, and social pressure factors for each historical year are interpreted year by year, forming a historical time series dataset.

[0103] Optionally, the basic quantitative factor values ​​from historical periods are input into a multi-layered coupled analysis framework for dynamic extrapolation, resulting in a historical simulation scenario. The basic quantitative factor values ​​from the starting year of the historical simulation are used as the initial input for the dynamic extrapolation, while the social pressure factor values ​​from subsequent years are used as mandatory inputs that change year by year. The evolution trajectory of the ecological vulnerability factor in the historical simulation scenario is compared with the actual ecological conditions recorded in historical periods to calculate the trajectory fit. The actual recorded ecological conditions are derived from the actual values ​​of the ecological vulnerability factor in the historical time-series dataset. Trajectory fit... Calculated using the following formula:

[0104]

[0105] Where: symbol Represents the degree of trajectory matching, symbol The symbol represents the total number of historical years included in the comparison. Representing the The comprehensive state index of ecological vulnerability factors in historical simulation scenarios, with symbols... Representing the The annual ecological vulnerability factor is a comprehensive state index interpreted from actual observation data. The summation term calculates the absolute value of the relative error between the simulated value and the observed value for each year. The average value reflects the average relative error. Subtracting this value from 1 yields the degree of agreement.

[0106] It is understandable that if the trajectory matching degree reaches the preset standard, the multi-layer coupling analysis framework and dynamic simulation are deemed effective; otherwise, the dynamic rules are adjusted, and the dynamic simulation is repeated until the trajectory matching degree meets the standard. The preset standard can be set as the trajectory matching degree. Greater than or equal to 0.85. If the calculated value is... If the value is below 0.85, return to check and adjust the state transition function of the ecological vulnerability factor described in the example. The specific form or parameters are determined, and then the same historical data is used to re-perform dynamic simulation and calculate a new trajectory fit. This process is iterated until the fit meets the preset standard. Adjustments may involve changing the support strength, the comprehensive shaping effect evaluation value, and the influence coefficient or functional relationship of the comprehensive stress index on the rate of change of ecological vulnerability indicators.

[0107] It is understandable that identifying element combination patterns is a process of extracting beneficial patterns from a large amount of simulation output, and the results provide specific quantitative control target ranges for ecological and geological environmental safety management. The verification step is a process of inverting and verifying the constructed analytical framework and deductive logic using historical facts, and it is a necessary step to ensure the reliability and credibility of the entire quantitative analysis method. The identification results of element combination patterns depend on the completeness of the simulation scenario set and the rationality of the dynamic rules, and the verification step is the key to evaluating and calibrating this rationality.

[0108] See Figure 5 This is a box plot of the scenario stability of ecological and geological environmental factors in mountainous rural areas. It visually presents the correspondence between "high geological stability factor → stable scenario" and "high social pressure factor → unstable scenario," representing a quantitative mapping of "factor numerical combination → scenario stability." It clarifies that social pressure factor is the core driving factor leading to scenario instability, while geological stability factor is the key supporting factor for maintaining scenario stability. Differentiated control schemes can be formulated based on the scenario distribution of different factors to promote the transformation of the ecological and geological environment towards a stable scenario. Through the factor numerical distribution under different scenarios, specific control targets such as "reducing the social pressure factor from 0.7 to 0.4 and increasing the geological stability factor from 0.6 to 0.8" can be quantified, providing an executable numerical path for the safe optimization of the ecological and geological environment.

[0109] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0110] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for quantitative analysis of ecological and geological environmental safety in mountainous rural areas, characterized in that, The method includes: Obtain raw observation data of the area to be analyzed, including geological bodies, landforms, vegetation, water systems, and human settlements. The original observation data were interpreted to extract four basic quantitative factors: geological stability factors, landform factors, ecological vulnerability factors, and social pressure factors. A multi-layered coupled analysis framework is constructed, with geological stability factors as the base, landform factors as the overlay, ecological vulnerability factors as the medium, and social pressure factors as the disturbance source. Within the multi-layered coupling analysis framework, ecological vulnerability factors are used as the core transmission link to calculate the supporting strength of geological stability factors, the shaping effect of landform factors, and the stress index of social pressure factors. Based on support strength, shaping effect and stress index, dynamic simulation is carried out within a multi-layered coupled analysis framework to generate a set of simulated scenarios that reflect the evolution path of environmental state under different combinations of factors. The combination patterns of elements that stabilize ecological vulnerability factors are identified from the set of simulated scenarios. The specific numerical ranges of various basic quantitative factors corresponding to the combination patterns are determined as the quantitative characterization results of ecological geological environment safety.

2. The method for quantitative analysis of ecological and geological environmental safety in mountainous rural areas according to claim 1, characterized in that, The element interpretation of the original observation data specifically includes: Based on the original observation data related to geological bodies, the types of rock and soil bodies, structural features and groundwater occurrence characteristics are identified. The identification results are quantified into rock and soil strength values, tectonic activity indicators and aquifer water-bearing levels, which are then combined to form the geological stability factor. Based on the original observation data related to landforms, the topographic relief, gully cutting density and slope structure characteristics are analyzed. The analysis results are quantified into topographic complexity, gully density and slope stability scores, which are then combined to form the landform factors mentioned above. Based on the original observation data related to vegetation and water system, vegetation community coverage, biodiversity index, river network stability and water body self-purification capacity are assessed. The assessment results are quantified as vegetation coverage, species abundance, river network stability coefficient and water quality maintenance coefficient, which are combined to form the ecological vulnerability factors mentioned above. Based on the original observation data related to human settlement elements, the population density, land use intensity, scale of engineering activities and pollution emission load are statistically analyzed. The statistical results are quantified into population pressure value, land use index, engineering disturbance intensity and pollution load index, which are combined to form the social pressure factor.

3. The method for quantitative analysis of ecological and geological environmental safety in mountainous rural areas according to claim 2, characterized in that, Calculating the support strength of the geological stability factor for the ecological vulnerability factor includes: Establish a correlation mapping between the soil and rock strength values, tectonic activity indicators and the vegetation coverage and species abundance; Through correlation mapping analysis, the lower limit of soil and rock strength and the upper limit of tectonic activity index required to maintain the preset levels of vegetation cover and species abundance were determined. The support strength value of the geological stability factor for the ecological vulnerability factor is calculated by taking into account the extent to which the actual soil and rock strength value exceeds the lower limit and the extent to which the actual tectonic activity index is lower than the upper limit.

4. The method for quantitative analysis of ecological and geological environmental safety in mountainous rural areas according to claim 3, characterized in that, Calculating the shaping effect of landform factors on the ecological vulnerability factors includes: Establish a correlation mapping between the terrain complexity, gully density, and slope stability scores and the river network stability coefficient and water quality maintenance coefficient; Through correlation mapping analysis, the influence trends of different combinations of terrain complexity, gully density and slope stability scores on river network stability coefficient and water quality maintenance coefficient were evaluated. Based on this trend, the comprehensive shaping effect of the current landform factor configuration on the river network stability coefficient and water quality maintenance coefficient among the ecologically vulnerable factors is calculated.

5. The method for quantitative analysis of ecological and geological environmental safety in mountainous rural areas according to claim 4, characterized in that, Calculating the stress index of social stress factors on the ecological vulnerability factors includes: Establish a correlation mapping between the population pressure value, land use index, engineering disturbance intensity and pollution load index and the vegetation coverage, species abundance, river network stability coefficient and water quality maintenance coefficient; Through correlation mapping analysis, the rate of decline of vegetation cover, species abundance, river network stability coefficient or water quality maintenance coefficient caused by the increase of pressure value per unit population, land use index, engineering disturbance intensity or pollution load index is quantified. Based on the specific values ​​of current social stress factors and the decay rate, a weighted comprehensive stress index of social stress factors on ecological vulnerability factors is calculated.

6. The method for quantitative analysis of ecological and geological environmental safety in mountainous rural areas according to claim 5, characterized in that, Based on support strength, shaping effect, and stress index, dynamic simulations are performed within a multi-layered coupled analysis framework to generate a set of simulated scenarios reflecting the evolution path of environmental states under different combinations of factors. Specifically, these scenarios include: Within the aforementioned multi-layered coupling analysis framework, the values ​​of the geological stability factor, landform factor, and social pressure factor are set to vary within their possible ranges according to a set step size. For each combination of changing factor values, the corresponding support strength, shaping effect, and stress index are recalculated according to the calculation methods for the support strength, the shaping effect, and the stress index. The recalculated support strength, shaping effect, and stress index are used as inputs to drive the iterative calculation of each quantitative indicator in the ecological vulnerability factor according to the preset dynamic rules. Record the state change trajectory of the ecological vulnerability factor during each iteration calculation, and construct the simulation scenario set by combining all factor values ​​and their corresponding state change trajectories.

7. The method for quantitative analysis of ecological and geological environmental safety in mountainous rural areas according to claim 6, characterized in that, The identification of element combinations that stabilize ecologically vulnerable factors from the simulated scenario set includes: In the set of simulated scenarios, all simulated scenarios in which the fluctuation range of each quantitative indicator of the ecological vulnerability factor is less than the preset threshold after the iterative calculation is completed are selected. The numerical distribution patterns of geological stability factors, landform factors, and social pressure factors in the selected simulation scenarios were analyzed. The numerical ranges of various factors that exhibit a high-frequency concentration trend in the numerical distribution pattern are defined as the element combination pattern.

8. The method for quantitative analysis of ecological and geological environmental safety in mountainous rural areas according to claim 7, characterized in that, After constructing the simulated scenario set, a verification step is also included: Actual observational data of the region to be analyzed during historical periods are selected and interpreted as basic quantitative factors for historical periods. The basic quantitative factor values ​​of historical periods are input into the multi-layered coupled analysis framework for dynamic simulation to obtain historical simulation scenarios. The evolution trajectory of ecologically vulnerable factors in historical simulation scenarios is compared with the actual ecological conditions recorded in historical periods, and the degree of trajectory consistency is calculated. If the trajectory matching degree reaches the preset standard, the multi-layer coupling analysis framework and dynamic inference are deemed effective; otherwise, the dynamic rules are adjusted and the dynamic inference is repeated until the trajectory matching degree reaches the standard.

9. The method for quantitative analysis of ecological and geological environmental safety in mountainous rural areas according to claim 1, characterized in that, The acquisition of raw observation data for the area to be analyzed includes geological features, landforms, vegetation, water systems, and human settlement elements, specifically including: Acquire regional multispectral and radar imagery data through satellite remote sensing platforms; Geological drilling, soil sampling, vegetation quadrat and water quality monitoring data were obtained through a ground survey network; Data on population and economy, land use planning, and infrastructure construction projects are obtained through social information statistics systems. Data from different sources are registered and fused in a unified spatiotemporal coordinate system to form spatiotemporally consistent original observation data.

10. A quantitative analysis system for the ecological and geological environmental safety of mountainous rural areas, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for quantitative analysis of ecological and geological environmental safety in mountainous rural areas as described in any one of claims 1 to 9.

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